Reinforcement Learning Methodology for Decision Analysis and Support in Long-term Care

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Our proposed work will develop new data analysis methodology based on reinforcement learning with our partner, the Canadian software company PointClickCare, which will be used to construct new analysis tools to better support decision-making in the long-term care industry.The health outcomes of residents in long-term care are affected by individual decisions made at the bedside, as well as by facility-level decisions made by administrators at the facilities where they receive care. The goal of this project is to develop data analysis methods that can use health record and administrative data to better understand what decision-making strategies, both at the individual and the facility level, are associated with the best outcomes for long-term care residents and staff, with a focus on infectious diseases. These new data analysis methods will be applied to data from long-term care facilities together with data about infectious disease outbreaks in order to develop software tools that help users understand what decision strategies at the individual and the facility levels are most effective, by taking into account information about residents, facilities, and the context where those facilities operate. The resulting tools will be incorporated into PointClickCare's electronic health record products and made available to their clients in Canada and internationally to allow them to better inform their decision-making and improve outcomes for their residents and staff.